Practical management science / Wayne L. Winston and S. Christian Albright ; with case studies by Mark Broadie.

By: Winston, Wayne L .

Contributor(s): Albright, S. Christian | Broadie, Mark Nathan .

Material type: materialTypeLabel

BookPublisher: Pacific Grove, CA : Brooks/Cole, 2001Edition: 2nd ed.Description: xviii, 953 p. : ill. ; 26 cm.ISBN: 0534371353.Subject(s): Management science -- Computer simulation | Management science -- Mathematical models | Electronic spreadsheets

DDC classification: 658.001

Contents:

Introduction to modeling -- Introductory spreadsheet modeling -- Introduction to optimization modeling -- Linear programming models -- Network models -- Linear optimization models with integer variables -- Nonlinear optimization models -- Evolutionary solver: An alternative optimization procedure -- Multi-objective decision making -- Decision making under uncertainty -- Introduction to simulation modeling -- Simulation models -- Inventory models -- Queueing models -- Regression analysis -- Time series analysis and forecasting.

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode	Item holds
General Lending	MTU Bishopstown Library Lending	658.001 (Browse shelf(Opens below))	1	Available		00086153
General Lending	MTU Bishopstown Library Lending	658.001 (Browse shelf(Opens below))	1	Available		00086132

Total holds: 0

Browsing MTU Bishopstown Library shelves, Shelving location: Lending Close shelf browser (Hides shelf browser)

Previous								Next
Previous	658 How to manage: the definitive guide to effective management /	658 How to manage: the definitive guide to effective management /	658.001 Practical management science /	658.001 Practical management science /	658.0019 Organizational behavior /	658.002462 Management for engineers, scientists and technologists /	658.002462 Management for engineers, technologists and scientists /	Next

Enhanced descriptions from Syndetics:

In the Second Edition of their popular text, Wayne Winston and Chris Albright continue to build on their highly successful approach of teaching by example while using spreadsheets to model a wide variety of business problems. The authors show the relevance of topics through numerous examples of real-world implementation of management science. The ideal solution for people who want to teach by example and who want to solve real problems with spreadsheets and professional spreadsheet add-ins, this text is always interesting, in part due to the useful cases added to this edition.

Includes bibliographical references (p. 943-947) and index.

Table of contents provided by Syndetics

Chapter 1 Introduction to Modeling (p. 1)
1.1 Introduction (p. 2)
1.2 A Waiting-Line Example (p. 3)
1.3 Modeling versus Models (p. 8)
1.4 The Seven-Step Modeling Process (p. 8)
1.5 Successful Management Science Applications (p. 14)
1.6 Why Study Management Science? (p. 21)
1.7 Software Included in This Book (p. 23)
1.8 Conclusion (p. 25)
Chapter 2 Introductory Spreadsheet Modeling (p. 27)
2.1 Introduction (p. 28)
2.2 Basic Spreadsheet Modeling Concepts (p. 29)
2.3 Modeling Examples (p. 30)
2.4 Conclusion (p. 58)
Appendix Tips for Editing and Documenting Spreadsheets (p. 62)
Chapter 3 Introduction to Optimization Modeling (p. 67)
3.1 Introduction (p. 68)
3.2 A Brief History of Linear Programming (p. 68)
3.3 Introduction to LP Modeling (p. 69)
3.4 Sensitivity Analysis and the Solver Table Add-In (p. 78)
3.5 The Linear Assumptions (p. 83)
3.6 Graphical Solution Method (p. 86)
3.7 Infeasibility and Unboundedness (p. 90)
3.8 A Multiperiod Production Problem (p. 91)
3.9 A Decision Support System (p. 98)
3.10 Conclusion (p. 100)
Appendix Information on Solvers (p. 105)
Case 3.1 Shelby Shelving (p. 108)
Chapter 4 Linear Programming Models (p. 111)
4.1 Introduction (p. 112)
4.2 Static Workforce Scheduling Models (p. 113)
4.3 Aggregate Planning Models (p. 120)
4.4 Dynamic Workforce Planning Models (p. 131)
4.5 Blending Models (p. 137)
4.6 Production Process Models (p. 146)
4.7 Dynamic Financial Models (p. 154)
4.8 Data Envelopment Analysis (DEA) (p. 162)
4.9 Conclusion (p. 170)
Case 4.1 Lakefield Corporation's Oil Trading Desk (p. 184)
Case 4.2 Foreign Currency Trading (p. 189)
Chapter 5 Network Models (p. 191)
5.1 Introduction (p. 192)
5.2 Transportation Models (p. 193)
5.3 More General Logistics Models (p. 208)
5.4 Non-Logistics Network Models (p. 223)
5.5 Project Scheduling Models (p. 251)
5.6 Conclusion (p. 262)
Case 5.1 Optimized Motor Carrier Selection at Westvaco (p. 271)
Chapter 6 Linear Optimization Models with Integer Variables (p. 275)
6.1 Introduction (p. 276)
6.2 Approaches to Optimization with Integer Variables (p. 277)
6.3 Capital Budgeting Models (p. 278)
6.4 Fixed-Cost Models (p. 290)
6.5 Lockbox Models (p. 300)
6.6 Plant and Warehouse Location Models (p. 306)
6.7 Set-Covering Models (p. 313)
6.8 Models with Either-Or Constraints (p. 319)
6.9 Cutting Stock Models (p. 323)
6.10 Conclusion (p. 327)
Case 6.1 Giant Motor Company I (p. 334)
Chapter 7 Nonlinear Optimization Models (p. 337)
7.1 Introduction (p. 338)
7.2 Basic Ideas of Nonlinear Optimization (p. 339)
7.3 Pricing Models (p. 342)
7.4 Sales Force Allocation Models (p. 355)
7.5 Facility Location Models (p. 359)
7.6 Rating Sports Teams (p. 364)
7.7 Estimating the Beta of a Stock (p. 369)
7.8 Portfolio Optimization (p. 375)
7.9 Conclusion (p. 392)
Case 7.1 GMS Stock Hedging (p. 395)
Case 7.2 Durham Asset Management (p. 397)
Chapter 8 Evolutionary Solver: An Alternative Optimization Procedure (p. 399)
8.1 Introduction (p. 401)
8.2 Introduction to Genetic Algorithms (p. 403)
8.3 Introduction to the Evolutionary Solver (p. 405)
8.4 Nonlinear Pricing Models (p. 410)
8.5 Combinatorial Models (p. 416)
8.6 Fitting an S-Shaped Curve (p. 426)
8.7 Portfolio Optimization (p. 431)
8.8 Cluster Analysis (p. 433)
8.9 Discriminant Analysis (p. 438)
8.10 Conclusion (p. 442)
Case 8.1 Assigning MBA Students to Teams (p. 447)
Chapter 9 Multi-Objective Decision Making (p. 449)
9.1 Introduction (p. 450)
9.2 Goal Programming (p. 451)
9.3 Pareto Optimality and Trade-off Curves (p. 463)
9.4 The Analytic Hierarchy Process (p. 472)
9.5 Conclusion (p. 485)
Case 9.1 Play Time Toy Company (p. 490)
Chapter 10 Decision Making Under Uncertainty (p. 493)
10.1 Introduction (p. 494)
10.2 Elements of a Decision Analysis (p. 496)
10.3 More Single-Stage Examples (p. 514)
10.4 Multistage Decision Problems (p. 524)
10.5 Bayes' Rule (p. 532)
10.6 Incorporating Attitudes Toward Risk (p. 540)
10.7 Conclusion (p. 549)
Case 10.1 GMC Motor Company II (p. 558)
Case 10.2 Jogger Shoe Company (p. 560)
Case 10.3 Westhouser Paper Company (p. 561)
Chapter 11 Introduction to Simulation Modeling (p. 563)
11.1 Introduction (p. 564)
11.2 Real Applications of Simulation (p. 565)
11.3 Generating Uniformly Distributed Random Numbers (p. 567)
11.4 Simulation with Built-In Excel Tools (p. 570)
11.5 Generating Random Numbers from Other Probability Distributions (p. 580)
11.6 Introduction to @ Risk (p. 582)
11.7 Correlation in @ Risk (p. 600)
11.8 Conclusion (p. 609)
Case 11.1 Ski Jacket Production (p. 614)
Case 11.2 Ebony Bath Soap (p. 615)
Chapter 12 Simulation Models (p. 617)
12.1 Introduction (p. 618)
12.2 Operations Models (p. 619)
12.3 Financial Models (p. 644)
12.4 Marketing Models (p. 672)
12.5 Simulating Games of Chance (p. 688)
12.6 Using TopRank with @ Risk for Powerful Modeling (p. 697)
12.7 Conclusion (p. 705)
Case 12.1 A College Fund Investment Decision (p. 710)
Case 12.2 Bond Investment Strategy (p. 711)
Case 12.3 Financials at Carco (p. 712)
Chapter 13 Inventory Models (p. 715)
13.1 Introduction (p. 716)
13.2 Categories of Inventory Models (p. 717)
13.3 Types of Costs in Inventory Models (p. 719)
13.4 Economic Order Quantity (EOQ) Models (p. 720)
13.5 Probabilistic Inventory Models (p. 738)
13.6 Ordering Simulation Models (p. 747)
13.7 Supply Chain Models (p. 753)
13.8 Conclusion (p. 759)
Case 13.1 Subway Token Hoarding (p. 762)
Case 13.2 Retail Pricing Using Retailer (p. 763)
Chapter 14 Queueing Models (p. 769)
14.1 Introduction (p. 770)
14.2 Elements of Queueing Models (p. 772)
14.3 The Exponential Distribution (p. 775)
14.4 Important Queueing Relationships (p. 780)
14.5 Analytical Models (p. 782)
14.6 Queueing Simulation (p. 799)
14.7 Conclusion (p. 817)
Case 14.1 The Catalog Company Problem (p. 821)
Chapter 15 Regression Analysis (p. 823)
15.1 Introduction (p. 825)
15.2 Scatterplots: Graphing Relationships (p. 827)
15.3 Correlations: Indicators of Linear Relationships (p. 831)
15.4 Simple Linear Regression (p. 832)
15.5 Multiple Regression (p. 839)
15.6 The Statistical Model (p. 844)
15.7 Inferences About the Regression Coefficients (p. 846)
15.8 Multicollinearity (p. 849)
15.9 Modeling Possibilities (p. 853)
15.10 Prediction (p. 872)
15.11 Conclusion (p. 874)
Case 15.1 Quantity Discounts at the FirmChair Company (p. 882)
Case 15.2 Demand for French Bread at Howie's (p. 883)
Case 15.3 Investing for Retirement (p. 884)
Case 15.4 Heating Oil at Dupree Fuels Company (p. 885)
Case 15.5 Forecasting Overhead at Wagner Printers (p. 886)
Chapter 16 Time Series Analysis and Forecasting (p. 887)
16.1 Introduction (p. 888)
16.2 General Concepts (p. 890)
16.3 Random Series (p. 891)
16.4 The Random Walk Model (p. 898)
16.5 Autoregression Models (p. 902)
16.6 Regression-Based Trend Models (p. 905)
16.7 Moving Averages (p. 912)
16.8 Exponential Smoothing (p. 917)
16.9 Deseasonalizing: The Ratio-to-Moving-Averages Method (p. 931)
16.10 Estimating Seasonality with Regression (p. 934)
16.11 Conclusion (p. 938)
Case 16.1 Arrivals at the Credit Union (p. 941)
Case 16.2 Forecasting Weekly Sales at Amanta (p. 942)
References (p. 943)
Index (p. 947)

Author notes provided by Syndetics

Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University
Chris Albright teaches in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University.